Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations46406
Missing cells250397
Missing cells (%)24.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 MiB
Average record size in memory176.0 B

Variable types

Categorical7
Numeric8
Text6
DateTime1

Alerts

IDADE_ESTIMADA has constant value "0.0" Constant
ATIVO is highly overall correlated with IDENTIDADE_GENERO_ID and 2 other fieldsHigh correlation
IDENTIDADE_GENERO_ID is highly overall correlated with ATIVOHigh correlation
NUMERO_TEL_CEL is highly overall correlated with ATIVOHigh correlation
ORIENTACAO_SEXUAL_ID is highly overall correlated with ATIVOHigh correlation
PESSOA_FISICA_ID is highly overall correlated with Prontuário SIAP ?High correlation
Prontuário SIAP ? is highly overall correlated with PESSOA_FISICA_IDHigh correlation
ATIVO is highly imbalanced (99.8%) Imbalance
NACIONALIDADE_ID is highly imbalanced (98.4%) Imbalance
SEXO_ID is highly imbalanced (68.5%) Imbalance
ORIENTACAO_SEXUAL_ID is highly imbalanced (90.7%) Imbalance
IDENTIDADE_GENERO_ID is highly imbalanced (96.5%) Imbalance
IDADE_ESTIMADA has 45227 (97.5%) missing values Missing
NUMERO_CPF has 34913 (75.2%) missing values Missing
NUMERO_RG has 36031 (77.6%) missing values Missing
NUMERO_TEL_CEL has 38959 (84.0%) missing values Missing
ESTADO_ECONOMICO_ID has 8427 (18.2%) missing values Missing
PROFISSAO_ID has 25667 (55.3%) missing values Missing
ORIENTACAO_SEXUAL_ID has 30527 (65.8%) missing values Missing
IDENTIDADE_GENERO_ID has 30527 (65.8%) missing values Missing

Reproduction

Analysis started2025-05-21 19:38:16.667983
Analysis finished2025-05-21 19:38:24.151223
Duration7.48 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ATIVO
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size362.7 KiB
ATIVO
46397 
0
 
8
SELECT CASE WHEN det.ativo = 1 THEN 'ATIVO' ELSE TO_CHAR(det.ativo) END AS ATIVO, pf.PESSOA_FISICA_ID, det.DETENTO_ID as "Prontuário SIAP ?", det.NUMERO_PRONTUARIO, pf.NOME_COMPLETO, pf.DATA_NASCIMENTO, pf.IDADE_ESTIMADA, pf.NACIONALIDADE_ID, pf.NOME_MAE, pf.NOME_PAI, pf.NUMERO_CPF, pf.NUMERO_RG, pf.NUMERO_TEL_CEL, pf.TIPO_DEFICIENCIA_ID, --det.ENDERECO_ID, pf.ESCOLARIDADE_ID, pf.ESTADO_CIVIL_ID, pf.ESTADO_ECONOMICO_ID, pf.PROFISSAO_ID, pf.SEXO_ID, pf.ORIENTACAO_SEXUAL_ID, pf.IDENTIDADE_GENERO_ID, un.sigla FROM siap.detento det JOIN siap.pessoa_fisica pf ON pf.pessoa_fisica_id = det.pessoa_fisica_id JOIN siap.unidade un ON un.unidade_id = det.unidade_id WHERE un.sigla IN ('PATRONATO', 'URA')
 
1

Length

Max length815
Median length5
Mean length5.0167651
Min length1

Characters and Unicode

Total characters232808
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSELECT CASE WHEN det.ativo = 1 THEN 'ATIVO' ELSE TO_CHAR(det.ativo) END AS ATIVO, pf.PESSOA_FISICA_ID, det.DETENTO_ID as "Prontuário SIAP ?", det.NUMERO_PRONTUARIO, pf.NOME_COMPLETO, pf.DATA_NASCIMENTO, pf.IDADE_ESTIMADA, pf.NACIONALIDADE_ID, pf.NOME_MAE, pf.NOME_PAI, pf.NUMERO_CPF, pf.NUMERO_RG, pf.NUMERO_TEL_CEL, pf.TIPO_DEFICIENCIA_ID, --det.ENDERECO_ID, pf.ESCOLARIDADE_ID, pf.ESTADO_CIVIL_ID, pf.ESTADO_ECONOMICO_ID, pf.PROFISSAO_ID, pf.SEXO_ID, pf.ORIENTACAO_SEXUAL_ID, pf.IDENTIDADE_GENERO_ID, un.sigla FROM siap.detento det JOIN siap.pessoa_fisica pf ON pf.pessoa_fisica_id = det.pessoa_fisica_id JOIN siap.unidade un ON un.unidade_id = det.unidade_id WHERE un.sigla IN ('PATRONATO', 'URA')
2nd rowATIVO
3rd rowATIVO
4th rowATIVO
5th rowATIVO

Common Values

ValueCountFrequency (%)
ATIVO 46397
> 99.9%
0 8
 
< 0.1%
SELECT CASE WHEN det.ativo = 1 THEN 'ATIVO' ELSE TO_CHAR(det.ativo) END AS ATIVO, pf.PESSOA_FISICA_ID, det.DETENTO_ID as "Prontuário SIAP ?", det.NUMERO_PRONTUARIO, pf.NOME_COMPLETO, pf.DATA_NASCIMENTO, pf.IDADE_ESTIMADA, pf.NACIONALIDADE_ID, pf.NOME_MAE, pf.NOME_PAI, pf.NUMERO_CPF, pf.NUMERO_RG, pf.NUMERO_TEL_CEL, pf.TIPO_DEFICIENCIA_ID, --det.ENDERECO_ID, pf.ESCOLARIDADE_ID, pf.ESTADO_CIVIL_ID, pf.ESTADO_ECONOMICO_ID, pf.PROFISSAO_ID, pf.SEXO_ID, pf.ORIENTACAO_SEXUAL_ID, pf.IDENTIDADE_GENERO_ID, un.sigla FROM siap.detento det JOIN siap.pessoa_fisica pf ON pf.pessoa_fisica_id = det.pessoa_fisica_id JOIN siap.unidade un ON un.unidade_id = det.unidade_id WHERE un.sigla IN ('PATRONATO', 'URA') 1
 
< 0.1%

Length

2025-05-21T16:38:24.223829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T16:38:24.312812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ativo 46399
99.9%
0 8
 
< 0.1%
4
 
< 0.1%
join 2
 
< 0.1%
un.sigla 2
 
< 0.1%
on 2
 
< 0.1%
as 2
 
< 0.1%
pf.pessoa_fisica_id 2
 
< 0.1%
pf.estado_economico_id 1
 
< 0.1%
pf.profissao_id 1
 
< 0.1%
Other values (43) 43
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 46436
19.9%
O 46436
19.9%
A 46430
19.9%
T 46417
19.9%
V 46400
19.9%
144
 
0.1%
E 45
 
< 0.1%
_ 36
 
< 0.1%
. 32
 
< 0.1%
31
 
< 0.1%
Other values (42) 401
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 46436
19.9%
O 46436
19.9%
A 46430
19.9%
T 46417
19.9%
V 46400
19.9%
144
 
0.1%
E 45
 
< 0.1%
_ 36
 
< 0.1%
. 32
 
< 0.1%
31
 
< 0.1%
Other values (42) 401
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 46436
19.9%
O 46436
19.9%
A 46430
19.9%
T 46417
19.9%
V 46400
19.9%
144
 
0.1%
E 45
 
< 0.1%
_ 36
 
< 0.1%
. 32
 
< 0.1%
31
 
< 0.1%
Other values (42) 401
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 46436
19.9%
O 46436
19.9%
A 46430
19.9%
T 46417
19.9%
V 46400
19.9%
144
 
0.1%
E 45
 
< 0.1%
_ 36
 
< 0.1%
. 32
 
< 0.1%
31
 
< 0.1%
Other values (42) 401
 
0.2%

PESSOA_FISICA_ID
Real number (ℝ)

High correlation 

Distinct46404
Distinct (%)> 99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean201621.1
Minimum25
Maximum1034783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.7 KiB
2025-05-21T16:38:24.437447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile20070.6
Q184094
median153796
Q3166636
95-th percentile729696.8
Maximum1034783
Range1034758
Interquartile range (IQR)82542

Descriptive statistics

Standard deviation206380.25
Coefficient of variation (CV)1.0236044
Kurtosis3.115907
Mean201621.1
Median Absolute Deviation (MAD)62045
Skewness1.9420307
Sum9.3562273 × 109
Variance4.2592809 × 1010
MonotonicityNot monotonic
2025-05-21T16:38:24.536351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85893 2
 
< 0.1%
163851 1
 
< 0.1%
163841 1
 
< 0.1%
163843 1
 
< 0.1%
163844 1
 
< 0.1%
163845 1
 
< 0.1%
163846 1
 
< 0.1%
163848 1
 
< 0.1%
163849 1
 
< 0.1%
163850 1
 
< 0.1%
Other values (46394) 46394
> 99.9%
ValueCountFrequency (%)
25 1
< 0.1%
34 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
40 1
< 0.1%
43 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
49 1
< 0.1%
53 1
< 0.1%
ValueCountFrequency (%)
1034783 1
< 0.1%
1034765 1
< 0.1%
1034668 1
< 0.1%
1034644 1
< 0.1%
1034583 1
< 0.1%
1034565 1
< 0.1%
1034125 1
< 0.1%
1034105 1
< 0.1%
1034104 1
< 0.1%
1034103 1
< 0.1%

Prontuário SIAP ?
Real number (ℝ)

High correlation 

Distinct46405
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean141110.71
Minimum2
Maximum581037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.7 KiB
2025-05-21T16:38:24.626161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile18193.4
Q182192
median135872
Q3148726
95-th percentile400675
Maximum581037
Range581035
Interquartile range (IQR)66534

Descriptive statistics

Standard deviation104033.95
Coefficient of variation (CV)0.73725051
Kurtosis3.8832894
Mean141110.71
Median Absolute Deviation (MAD)39343
Skewness1.8426036
Sum6.5482426 × 109
Variance1.0823062 × 1010
MonotonicityNot monotonic
2025-05-21T16:38:24.713703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145923 1
 
< 0.1%
145926 1
 
< 0.1%
145927 1
 
< 0.1%
145928 1
 
< 0.1%
145929 1
 
< 0.1%
145931 1
 
< 0.1%
145932 1
 
< 0.1%
145933 1
 
< 0.1%
145934 1
 
< 0.1%
145935 1
 
< 0.1%
Other values (46395) 46395
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
11 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
21 1
< 0.1%
22 1
< 0.1%
26 1
< 0.1%
30 1
< 0.1%
ValueCountFrequency (%)
581037 1
< 0.1%
581036 1
< 0.1%
580995 1
< 0.1%
580980 1
< 0.1%
580936 1
< 0.1%
580935 1
< 0.1%
580735 1
< 0.1%
580720 1
< 0.1%
580719 1
< 0.1%
580718 1
< 0.1%
Distinct46389
Distinct (%)> 99.9%
Missing15
Missing (%)< 0.1%
Memory size362.7 KiB
2025-05-21T16:38:24.889247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.1448988
Min length2

Characters and Unicode

Total characters331459
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46387 ?
Unique (%)> 99.9%

Sample

1st row4005332
2nd row4005369
3rd row4005380
4th row4005439
5th row4005446
ValueCountFrequency (%)
2012187 2
 
< 0.1%
4002340 2
 
< 0.1%
2037116 1
 
< 0.1%
4009477 1
 
< 0.1%
4005730 1
 
< 0.1%
4005723 1
 
< 0.1%
4005523 1
 
< 0.1%
4005369 1
 
< 0.1%
4005380 1
 
< 0.1%
4005439 1
 
< 0.1%
Other values (46379) 46379
> 99.9%
2025-05-21T16:38:25.154071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 86303
26.0%
2 50042
15.1%
1 36370
11.0%
3 24979
 
7.5%
4 24637
 
7.4%
5 24159
 
7.3%
7 23695
 
7.1%
6 22874
 
6.9%
8 20579
 
6.2%
9 17770
 
5.4%
Other values (3) 51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 86303
26.0%
2 50042
15.1%
1 36370
11.0%
3 24979
 
7.5%
4 24637
 
7.4%
5 24159
 
7.3%
7 23695
 
7.1%
6 22874
 
6.9%
8 20579
 
6.2%
9 17770
 
5.4%
Other values (3) 51
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 86303
26.0%
2 50042
15.1%
1 36370
11.0%
3 24979
 
7.5%
4 24637
 
7.4%
5 24159
 
7.3%
7 23695
 
7.1%
6 22874
 
6.9%
8 20579
 
6.2%
9 17770
 
5.4%
Other values (3) 51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 86303
26.0%
2 50042
15.1%
1 36370
11.0%
3 24979
 
7.5%
4 24637
 
7.4%
5 24159
 
7.3%
7 23695
 
7.1%
6 22874
 
6.9%
8 20579
 
6.2%
9 17770
 
5.4%
Other values (3) 51
 
< 0.1%
Distinct43177
Distinct (%)93.0%
Missing1
Missing (%)< 0.1%
Memory size362.7 KiB
2025-05-21T16:38:25.308208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length100
Median length92
Mean length26.323995
Min length5

Characters and Unicode

Total characters1221565
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41527 ?
Unique (%)89.5%

Sample

1st rowMARIA LÚCIA IRACI NASCIMENTO DE LIMA
2nd rowMARIA DA CONCEIÇÃO DOS SANTOS
3rd rowGILVÂNIA MELQUÍADES DA SILVA
4th rowERICA IRIS DE SANTANA ALBUQUERQUE
5th rowMAIZA SEVERINA DA SILVA
ValueCountFrequency (%)
silva 21257
 
10.7%
da 20312
 
10.2%
de 12381
 
6.2%
jose 7232
 
3.6%
santos 5699
 
2.9%
dos 4560
 
2.3%
lima 2629
 
1.3%
oliveira 2534
 
1.3%
ferreira 2330
 
1.2%
souza 2229
 
1.1%
Other values (9527) 118038
59.3%
2025-05-21T16:38:25.620123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
152995
12.5%
A 152560
12.5%
O 109675
9.0%
E 101691
8.3%
I 95549
7.8%
S 95359
7.8%
R 75086
 
6.1%
L 71383
 
5.8%
D 66827
 
5.5%
N 63396
 
5.2%
Other values (86) 237044
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1221565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
152995
12.5%
A 152560
12.5%
O 109675
9.0%
E 101691
8.3%
I 95549
7.8%
S 95359
7.8%
R 75086
 
6.1%
L 71383
 
5.8%
D 66827
 
5.5%
N 63396
 
5.2%
Other values (86) 237044
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1221565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
152995
12.5%
A 152560
12.5%
O 109675
9.0%
E 101691
8.3%
I 95549
7.8%
S 95359
7.8%
R 75086
 
6.1%
L 71383
 
5.8%
D 66827
 
5.5%
N 63396
 
5.2%
Other values (86) 237044
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1221565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
152995
12.5%
A 152560
12.5%
O 109675
9.0%
E 101691
8.3%
I 95549
7.8%
S 95359
7.8%
R 75086
 
6.1%
L 71383
 
5.8%
D 66827
 
5.5%
N 63396
 
5.2%
Other values (86) 237044
19.4%
Distinct14792
Distinct (%)31.9%
Missing1
Missing (%)< 0.1%
Memory size362.7 KiB
Minimum1899-12-30 00:00:00
Maximum2006-04-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-21T16:38:25.711888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:25.803817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

IDADE_ESTIMADA
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing45227
Missing (%)97.5%
Memory size362.7 KiB
0.0
1179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3537
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1179
 
2.5%
(Missing) 45227
97.5%

Length

2025-05-21T16:38:25.890354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T16:38:25.947893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1179
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2358
66.7%
. 1179
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2358
66.7%
. 1179
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2358
66.7%
. 1179
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2358
66.7%
. 1179
33.3%

NACIONALIDADE_ID
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing80
Missing (%)0.2%
Memory size362.7 KiB
1.0
46189 
4.0
 
68
3.0
 
67
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters138978
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 46189
99.5%
4.0 68
 
0.1%
3.0 67
 
0.1%
2.0 2
 
< 0.1%
(Missing) 80
 
0.2%

Length

2025-05-21T16:38:26.006905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T16:38:26.069435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 46189
99.7%
4.0 68
 
0.1%
3.0 67
 
0.1%
2.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 46326
33.3%
0 46326
33.3%
1 46189
33.2%
4 68
 
< 0.1%
3 67
 
< 0.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 46326
33.3%
0 46326
33.3%
1 46189
33.2%
4 68
 
< 0.1%
3 67
 
< 0.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 46326
33.3%
0 46326
33.3%
1 46189
33.2%
4 68
 
< 0.1%
3 67
 
< 0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 46326
33.3%
0 46326
33.3%
1 46189
33.2%
4 68
 
< 0.1%
3 67
 
< 0.1%
2 2
 
< 0.1%
Distinct36591
Distinct (%)78.9%
Missing1
Missing (%)< 0.1%
Memory size362.7 KiB
2025-05-21T16:38:26.203977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length99
Median length82
Mean length25.66732
Min length1

Characters and Unicode

Total characters1191092
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33295 ?
Unique (%)71.7%

Sample

1st rowSEVERINA IRACI DO NASCIMENTO
2nd rowMARIA FILOMENA DOS SANTOS
3rd rowGENILDA ALVES DA SILVA
4th rowMARTA MARIA DE SANTANA
5th rowSEVERINA MARIA DA SILVA
ValueCountFrequency (%)
maria 22847
 
11.5%
da 22139
 
11.1%
silva 19291
 
9.7%
de 13227
 
6.7%
santos 5001
 
2.5%
dos 4397
 
2.2%
do 3128
 
1.6%
jose 2444
 
1.2%
lima 2339
 
1.2%
oliveira 2174
 
1.1%
Other values (7671) 101804
51.2%
2025-05-21T16:38:26.446291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 205179
17.2%
152898
12.8%
I 114198
9.6%
E 98309
8.3%
R 81627
 
6.9%
S 77099
 
6.5%
O 65699
 
5.5%
D 63323
 
5.3%
L 56429
 
4.7%
N 52839
 
4.4%
Other values (72) 223492
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1191092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 205179
17.2%
152898
12.8%
I 114198
9.6%
E 98309
8.3%
R 81627
 
6.9%
S 77099
 
6.5%
O 65699
 
5.5%
D 63323
 
5.3%
L 56429
 
4.7%
N 52839
 
4.4%
Other values (72) 223492
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1191092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 205179
17.2%
152898
12.8%
I 114198
9.6%
E 98309
8.3%
R 81627
 
6.9%
S 77099
 
6.5%
O 65699
 
5.5%
D 63323
 
5.3%
L 56429
 
4.7%
N 52839
 
4.4%
Other values (72) 223492
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1191092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 205179
17.2%
152898
12.8%
I 114198
9.6%
E 98309
8.3%
R 81627
 
6.9%
S 77099
 
6.5%
O 65699
 
5.5%
D 63323
 
5.3%
L 56429
 
4.7%
N 52839
 
4.4%
Other values (72) 223492
18.8%
Distinct32851
Distinct (%)70.8%
Missing2
Missing (%)< 0.1%
Memory size362.7 KiB
2025-05-21T16:38:26.597879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length90
Median length71
Mean length22.942591
Min length1

Characters and Unicode

Total characters1064628
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29627 ?
Unique (%)63.8%

Sample

1st rowJOÃO LÚCIO SOBRINHO
2nd row0
3rd rowJOSÉ MELQUÍADES DA SILVA
4th rowROBERTO KENNEDY DE ALBUQUERQUE
5th row0
ValueCountFrequency (%)
da 16461
 
9.6%
silva 15958
 
9.3%
de 10541
 
6.1%
jose 9803
 
5.7%
declarado 4859
 
2.8%
santos 3912
 
2.3%
dos 3749
 
2.2%
antonio 3242
 
1.9%
severino 2785
 
1.6%
manoel 2291
 
1.3%
Other values (5891) 98555
57.2%
2025-05-21T16:38:26.839702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 126607
11.9%
126231
11.9%
O 108570
10.2%
E 86483
 
8.1%
I 78777
 
7.4%
S 76781
 
7.2%
D 62634
 
5.9%
R 61086
 
5.7%
L 56523
 
5.3%
N 55724
 
5.2%
Other values (72) 225212
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1064628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 126607
11.9%
126231
11.9%
O 108570
10.2%
E 86483
 
8.1%
I 78777
 
7.4%
S 76781
 
7.2%
D 62634
 
5.9%
R 61086
 
5.7%
L 56523
 
5.3%
N 55724
 
5.2%
Other values (72) 225212
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1064628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 126607
11.9%
126231
11.9%
O 108570
10.2%
E 86483
 
8.1%
I 78777
 
7.4%
S 76781
 
7.2%
D 62634
 
5.9%
R 61086
 
5.7%
L 56523
 
5.3%
N 55724
 
5.2%
Other values (72) 225212
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1064628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 126607
11.9%
126231
11.9%
O 108570
10.2%
E 86483
 
8.1%
I 78777
 
7.4%
S 76781
 
7.2%
D 62634
 
5.9%
R 61086
 
5.7%
L 56523
 
5.3%
N 55724
 
5.2%
Other values (72) 225212
21.2%

NUMERO_CPF
Text

Missing 

Distinct11476
Distinct (%)99.9%
Missing34913
Missing (%)75.2%
Memory size362.7 KiB
2025-05-21T16:38:26.978274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length12.163317
Min length3

Characters and Unicode

Total characters139793
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11459 ?
Unique (%)99.7%

Sample

1st row07896490471
2nd row09163294451
3rd row49251872449
4th row45038945449
5th row71244383490
ValueCountFrequency (%)
702.296.844-67 2
 
< 0.1%
10005354447 2
 
< 0.1%
07236692441 2
 
< 0.1%
052.040.014-33 2
 
< 0.1%
70339614480 2
 
< 0.1%
informado 2
 
< 0.1%
70257097430 2
 
< 0.1%
088.231.754-70 2
 
< 0.1%
11460014448 2
 
< 0.1%
nao 2
 
< 0.1%
Other values (11475) 11483
99.8%
2025-05-21T16:38:27.200951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 20951
15.0%
0 18813
13.5%
1 13380
9.6%
7 11667
8.3%
8 10747
7.7%
3 10326
7.4%
9 10289
7.4%
2 10200
7.3%
5 9972
7.1%
6 9947
7.1%
Other values (19) 13501
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 20951
15.0%
0 18813
13.5%
1 13380
9.6%
7 11667
8.3%
8 10747
7.7%
3 10326
7.4%
9 10289
7.4%
2 10200
7.3%
5 9972
7.1%
6 9947
7.1%
Other values (19) 13501
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 20951
15.0%
0 18813
13.5%
1 13380
9.6%
7 11667
8.3%
8 10747
7.7%
3 10326
7.4%
9 10289
7.4%
2 10200
7.3%
5 9972
7.1%
6 9947
7.1%
Other values (19) 13501
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 20951
15.0%
0 18813
13.5%
1 13380
9.6%
7 11667
8.3%
8 10747
7.7%
3 10326
7.4%
9 10289
7.4%
2 10200
7.3%
5 9972
7.1%
6 9947
7.1%
Other values (19) 13501
9.7%

NUMERO_RG
Text

Missing 

Distinct10372
Distinct (%)> 99.9%
Missing36031
Missing (%)77.6%
Memory size362.7 KiB
2025-05-21T16:38:27.359099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length15
Median length7
Mean length7.3290602
Min length5

Characters and Unicode

Total characters76039
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10369 ?
Unique (%)99.9%

Sample

1st row7601520
2nd row8326972
3rd row2762058
4th row2014180
5th row5971232
ValueCountFrequency (%)
6389951 2
 
< 0.1%
9 2
 
< 0.1%
2 2
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
7472209 2
 
< 0.1%
10429524 2
 
< 0.1%
7753668 1
 
< 0.1%
7965967 1
 
< 0.1%
8995247 1
 
< 0.1%
Other values (10378) 10378
99.8%
2025-05-21T16:38:27.591332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 8581
11.3%
8 8270
10.9%
1 7993
10.5%
7 7989
10.5%
0 7758
10.2%
6 7290
9.6%
3 6869
9.0%
5 6862
9.0%
4 6845
9.0%
2 6431
8.5%
Other values (12) 1151
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 8581
11.3%
8 8270
10.9%
1 7993
10.5%
7 7989
10.5%
0 7758
10.2%
6 7290
9.6%
3 6869
9.0%
5 6862
9.0%
4 6845
9.0%
2 6431
8.5%
Other values (12) 1151
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 8581
11.3%
8 8270
10.9%
1 7993
10.5%
7 7989
10.5%
0 7758
10.2%
6 7290
9.6%
3 6869
9.0%
5 6862
9.0%
4 6845
9.0%
2 6431
8.5%
Other values (12) 1151
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 8581
11.3%
8 8270
10.9%
1 7993
10.5%
7 7989
10.5%
0 7758
10.2%
6 7290
9.6%
3 6869
9.0%
5 6862
9.0%
4 6845
9.0%
2 6431
8.5%
Other values (12) 1151
 
1.5%

NUMERO_TEL_CEL
Real number (ℝ)

High correlation  Missing 

Distinct7423
Distinct (%)99.7%
Missing38959
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean7.9008873 × 1010
Minimum0
Maximum9.9993917 × 1010
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size362.7 KiB
2025-05-21T16:38:27.688874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.1969294 × 1010
Q18.1984674 × 1010
median8.1987351 × 1010
Q38.1993028 × 1010
95-th percentile8.199997 × 1010
Maximum9.9993917 × 1010
Range9.9993917 × 1010
Interquartile range (IQR)8354464.5

Descriptive statistics

Standard deviation1.5127779 × 1010
Coefficient of variation (CV)0.19146936
Kurtosis17.574574
Mean7.9008873 × 1010
Median Absolute Deviation (MAD)3321558
Skewness-4.3937554
Sum5.8837907 × 1014
Variance2.2884968 × 1020
MonotonicityNot monotonic
2025-05-21T16:38:27.847621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.198615329 × 10102
 
< 0.1%
8.198832172 × 10102
 
< 0.1%
8.198619585 × 10102
 
< 0.1%
8.198768052 × 10102
 
< 0.1%
8.19867753 × 10102
 
< 0.1%
8.198734554 × 10102
 
< 0.1%
8.198899773 × 10102
 
< 0.1%
8.198674202 × 10102
 
< 0.1%
8.198588352 × 10102
 
< 0.1%
8.19885314 × 10102
 
< 0.1%
Other values (7413) 7427
 
16.0%
(Missing) 38959
84.0%
ValueCountFrequency (%)
0 2
< 0.1%
8 1
< 0.1%
1986382385 1
< 0.1%
1988183302 1
< 0.1%
6282662787 1
< 0.1%
7193665152 1
< 0.1%
7491511820 1
< 0.1%
8130385010 1
< 0.1%
8130917726 1
< 0.1%
8132221400 1
< 0.1%
ValueCountFrequency (%)
9.99939173 × 10101
< 0.1%
9.81963979 × 10101
< 0.1%
9.599140384 × 10101
< 0.1%
9.198877999 × 10101
< 0.1%
9.198461025 × 10101
< 0.1%
9.198437924 × 10101
< 0.1%
9.198323732 × 10101
< 0.1%
8.999436332 × 10101
< 0.1%
8.999413606 × 10101
< 0.1%
8.999403733 × 10101
< 0.1%

TIPO_DEFICIENCIA_ID
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.6226269
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.7 KiB
2025-05-21T16:38:27.922169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q15
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.52288878
Coefficient of variation (CV)0.09299724
Kurtosis3.7690055
Mean5.6226269
Median Absolute Deviation (MAD)0
Skewness-1.2961445
Sum260918
Variance0.27341268
MonotonicityNot monotonic
2025-05-21T16:38:27.990164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 29446
63.5%
5 16671
35.9%
3 174
 
0.4%
4 78
 
0.2%
1 19
 
< 0.1%
2 17
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
1 19
 
< 0.1%
2 17
 
< 0.1%
3 174
 
0.4%
4 78
 
0.2%
5 16671
35.9%
6 29446
63.5%
ValueCountFrequency (%)
6 29446
63.5%
5 16671
35.9%
4 78
 
0.2%
3 174
 
0.4%
2 17
 
< 0.1%
1 19
 
< 0.1%

ESCOLARIDADE_ID
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing13
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.9455521
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.7 KiB
2025-05-21T16:38:28.053936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q35
95-th percentile8
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7124606
Coefficient of variation (CV)0.687473
Kurtosis6.4876408
Mean3.9455521
Median Absolute Deviation (MAD)1
Skewness2.404962
Sum183046
Variance7.3574423
MonotonicityNot monotonic
2025-05-21T16:38:28.119261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 22277
48.0%
5 5772
 
12.4%
6 4849
 
10.4%
1 4606
 
9.9%
2 3830
 
8.3%
14 2235
 
4.8%
4 1876
 
4.0%
7 571
 
1.2%
8 328
 
0.7%
15 20
 
< 0.1%
Other values (3) 29
 
0.1%
ValueCountFrequency (%)
1 4606
 
9.9%
2 3830
 
8.3%
3 22277
48.0%
4 1876
 
4.0%
5 5772
 
12.4%
6 4849
 
10.4%
7 571
 
1.2%
8 328
 
0.7%
9 12
 
< 0.1%
10 15
 
< 0.1%
ValueCountFrequency (%)
15 20
 
< 0.1%
14 2235
 
4.8%
11 2
 
< 0.1%
10 15
 
< 0.1%
9 12
 
< 0.1%
8 328
 
0.7%
7 571
 
1.2%
6 4849
10.4%
5 5772
12.4%
4 1876
 
4.0%

ESTADO_CIVIL_ID
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.2321086
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.7 KiB
2025-05-21T16:38:28.184255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median4
Q35
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3822716
Coefficient of variation (CV)0.32661535
Kurtosis1.9722397
Mean4.2321086
Median Absolute Deviation (MAD)1
Skewness-0.55530826
Sum196391
Variance1.9106749
MonotonicityNot monotonic
2025-05-21T16:38:28.260074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 21220
45.7%
5 16978
36.6%
1 4625
 
10.0%
7 2255
 
4.9%
2 578
 
1.2%
9 347
 
0.7%
6 217
 
0.5%
3 170
 
0.4%
8 15
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
1 4625
 
10.0%
2 578
 
1.2%
3 170
 
0.4%
4 21220
45.7%
5 16978
36.6%
6 217
 
0.5%
7 2255
 
4.9%
8 15
 
< 0.1%
9 347
 
0.7%
ValueCountFrequency (%)
9 347
 
0.7%
8 15
 
< 0.1%
7 2255
 
4.9%
6 217
 
0.5%
5 16978
36.6%
4 21220
45.7%
3 170
 
0.4%
2 578
 
1.2%
1 4625
 
10.0%

ESTADO_ECONOMICO_ID
Real number (ℝ)

Missing 

Distinct10
Distinct (%)< 0.1%
Missing8427
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean2.4800284
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.7 KiB
2025-05-21T16:38:28.330252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2670201
Coefficient of variation (CV)0.51088935
Kurtosis15.096385
Mean2.4800284
Median Absolute Deviation (MAD)0
Skewness3.5296551
Sum94189
Variance1.60534
MonotonicityNot monotonic
2025-05-21T16:38:28.395252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 24242
52.2%
3 8303
 
17.9%
1 2093
 
4.5%
4 1942
 
4.2%
9 932
 
2.0%
5 314
 
0.7%
6 120
 
0.3%
7 17
 
< 0.1%
8 11
 
< 0.1%
10 5
 
< 0.1%
(Missing) 8427
 
18.2%
ValueCountFrequency (%)
1 2093
 
4.5%
2 24242
52.2%
3 8303
 
17.9%
4 1942
 
4.2%
5 314
 
0.7%
6 120
 
0.3%
7 17
 
< 0.1%
8 11
 
< 0.1%
9 932
 
2.0%
10 5
 
< 0.1%
ValueCountFrequency (%)
10 5
 
< 0.1%
9 932
 
2.0%
8 11
 
< 0.1%
7 17
 
< 0.1%
6 120
 
0.3%
5 314
 
0.7%
4 1942
 
4.2%
3 8303
 
17.9%
2 24242
52.2%
1 2093
 
4.5%

PROFISSAO_ID
Real number (ℝ)

Missing 

Distinct578
Distinct (%)2.8%
Missing25667
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean416.87159
Minimum1
Maximum926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.7 KiB
2025-05-21T16:38:28.473871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51
Q1157
median405
Q3638
95-th percentile817
Maximum926
Range925
Interquartile range (IQR)481

Descriptive statistics

Standard deviation260.78058
Coefficient of variation (CV)0.62556572
Kurtosis-1.3667134
Mean416.87159
Median Absolute Deviation (MAD)246
Skewness-0.030712045
Sum8645500
Variance68006.51
MonotonicityNot monotonic
2025-05-21T16:38:28.561510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 1411
 
3.0%
638 923
 
2.0%
157 884
 
1.9%
594 856
 
1.8%
6 855
 
1.8%
346 839
 
1.8%
99 782
 
1.7%
738 672
 
1.4%
731 616
 
1.3%
581 552
 
1.2%
Other values (568) 12349
26.6%
(Missing) 25667
55.3%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%
4 28
 
0.1%
5 2
 
< 0.1%
6 855
1.8%
26 1
 
< 0.1%
27 2
 
< 0.1%
28 24
 
0.1%
30 2
 
< 0.1%
ValueCountFrequency (%)
926 70
0.2%
886 1
 
< 0.1%
866 101
0.2%
858 52
 
0.1%
857 3
 
< 0.1%
855 1
 
< 0.1%
852 2
 
< 0.1%
851 132
0.3%
850 13
 
< 0.1%
848 28
 
0.1%

SEXO_ID
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size362.7 KiB
1.0
43769 
2.0
 
2636

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters139215
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 43769
94.3%
2.0 2636
 
5.7%
(Missing) 1
 
< 0.1%

Length

2025-05-21T16:38:28.637086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T16:38:28.697142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 43769
94.3%
2.0 2636
 
5.7%

Most occurring characters

ValueCountFrequency (%)
. 46405
33.3%
0 46405
33.3%
1 43769
31.4%
2 2636
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 46405
33.3%
0 46405
33.3%
1 43769
31.4%
2 2636
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 46405
33.3%
0 46405
33.3%
1 43769
31.4%
2 2636
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 46405
33.3%
0 46405
33.3%
1 43769
31.4%
2 2636
 
1.9%

ORIENTACAO_SEXUAL_ID
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing30527
Missing (%)65.8%
Memory size362.7 KiB
26.0
15530 
29.0
 
162
28.0
 
95
27.0
 
92

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters63516
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26.0
2nd row26.0
3rd row26.0
4th row29.0
5th row26.0

Common Values

ValueCountFrequency (%)
26.0 15530
33.5%
29.0 162
 
0.3%
28.0 95
 
0.2%
27.0 92
 
0.2%
(Missing) 30527
65.8%

Length

2025-05-21T16:38:28.760670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T16:38:28.830276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26.0 15530
97.8%
29.0 162
 
1.0%
28.0 95
 
0.6%
27.0 92
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 15879
25.0%
. 15879
25.0%
0 15879
25.0%
6 15530
24.5%
9 162
 
0.3%
8 95
 
0.1%
7 92
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15879
25.0%
. 15879
25.0%
0 15879
25.0%
6 15530
24.5%
9 162
 
0.3%
8 95
 
0.1%
7 92
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15879
25.0%
. 15879
25.0%
0 15879
25.0%
6 15530
24.5%
9 162
 
0.3%
8 95
 
0.1%
7 92
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15879
25.0%
. 15879
25.0%
0 15879
25.0%
6 15530
24.5%
9 162
 
0.3%
8 95
 
0.1%
7 92
 
0.1%

IDENTIDADE_GENERO_ID
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing30527
Missing (%)65.8%
Memory size362.7 KiB
26.0
15768 
29.0
 
53
28.0
 
34
27.0
 
24

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters63516
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26.0
2nd row26.0
3rd row26.0
4th row26.0
5th row26.0

Common Values

ValueCountFrequency (%)
26.0 15768
34.0%
29.0 53
 
0.1%
28.0 34
 
0.1%
27.0 24
 
0.1%
(Missing) 30527
65.8%

Length

2025-05-21T16:38:28.904295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T16:38:28.972821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26.0 15768
99.3%
29.0 53
 
0.3%
28.0 34
 
0.2%
27.0 24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 15879
25.0%
. 15879
25.0%
0 15879
25.0%
6 15768
24.8%
9 53
 
0.1%
8 34
 
0.1%
7 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15879
25.0%
. 15879
25.0%
0 15879
25.0%
6 15768
24.8%
9 53
 
0.1%
8 34
 
0.1%
7 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15879
25.0%
. 15879
25.0%
0 15879
25.0%
6 15768
24.8%
9 53
 
0.1%
8 34
 
0.1%
7 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15879
25.0%
. 15879
25.0%
0 15879
25.0%
6 15768
24.8%
9 53
 
0.1%
8 34
 
0.1%
7 24
 
< 0.1%

SIGLA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size362.7 KiB
URA
23931 
PATRONATO
22474 

Length

Max length9
Median length3
Mean length5.9058076
Min length3

Characters and Unicode

Total characters274059
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURA
2nd rowURA
3rd rowPATRONATO
4th rowPATRONATO
5th rowPATRONATO

Common Values

ValueCountFrequency (%)
URA 23931
51.6%
PATRONATO 22474
48.4%
(Missing) 1
 
< 0.1%

Length

2025-05-21T16:38:29.048363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T16:38:29.111888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ura 23931
51.6%
patronato 22474
48.4%

Most occurring characters

ValueCountFrequency (%)
A 68879
25.1%
R 46405
16.9%
T 44948
16.4%
O 44948
16.4%
U 23931
 
8.7%
P 22474
 
8.2%
N 22474
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 274059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 68879
25.1%
R 46405
16.9%
T 44948
16.4%
O 44948
16.4%
U 23931
 
8.7%
P 22474
 
8.2%
N 22474
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 274059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 68879
25.1%
R 46405
16.9%
T 44948
16.4%
O 44948
16.4%
U 23931
 
8.7%
P 22474
 
8.2%
N 22474
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 274059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 68879
25.1%
R 46405
16.9%
T 44948
16.4%
O 44948
16.4%
U 23931
 
8.7%
P 22474
 
8.2%
N 22474
 
8.2%

Interactions

2025-05-21T16:38:22.753620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:18.990869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.585931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.129216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.633905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.147994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.728719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.257462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.815159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.069376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.653471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.192228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.698081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.211522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.795638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.320976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.880239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.141891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.722006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.257761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.765685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.278533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.863322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.384974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.940778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.259429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.791019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.319364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.828841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.343240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.931936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.445597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:23.001783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.324203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.857628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.380282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.890847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.407765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.996871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.507137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:23.062317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.390217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.928198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.443834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.955468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.473762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.062393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.569158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:23.131010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.461730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.003173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.511363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.024982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.607833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.131943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.638693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:23.189011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:19.522923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.065695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:20.572366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.081982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:21.667103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.193948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T16:38:22.695091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-21T16:38:29.167893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ATIVOESCOLARIDADE_IDESTADO_CIVIL_IDESTADO_ECONOMICO_IDIDENTIDADE_GENERO_IDNACIONALIDADE_IDNUMERO_TEL_CELORIENTACAO_SEXUAL_IDPESSOA_FISICA_IDPROFISSAO_IDProntuário SIAP ?SEXO_IDSIGLATIPO_DEFICIENCIA_ID
ATIVO1.0000.0000.0000.0171.0000.0001.0001.0000.0000.0410.0000.0000.0100.000
ESCOLARIDADE_ID0.0001.0000.1010.1160.0000.0350.0380.0100.1150.0570.1150.0700.235-0.059
ESTADO_CIVIL_ID0.0000.1011.000-0.0610.0270.023-0.0370.042-0.0940.029-0.0940.1420.1710.037
ESTADO_ECONOMICO_ID0.0170.116-0.0611.0000.0000.032-0.0200.0620.0240.0730.0240.1760.1820.273
IDENTIDADE_GENERO_ID1.0000.0000.0270.0001.0000.0000.0810.3630.0200.0350.0190.0910.0220.017
NACIONALIDADE_ID0.0000.0350.0230.0320.0001.0000.0000.0000.0270.0000.0310.0270.0360.018
NUMERO_TEL_CEL1.0000.038-0.037-0.0200.0810.0001.0000.0000.094-0.0260.0950.0340.122-0.074
ORIENTACAO_SEXUAL_ID1.0000.0100.0420.0620.3630.0000.0001.0000.0220.0730.0700.4280.0480.014
PESSOA_FISICA_ID0.0000.115-0.0940.0240.0200.0270.0940.0221.0000.1401.0000.0720.451-0.233
PROFISSAO_ID0.0410.0570.0290.0730.0350.000-0.0260.0730.1401.0000.1400.3470.272-0.120
Prontuário SIAP ?0.0000.115-0.0940.0240.0190.0310.0950.0701.0000.1401.0000.2490.449-0.232
SEXO_ID0.0000.0700.1420.1760.0910.0270.0340.4280.0720.3470.2491.0000.0510.000
SIGLA0.0100.2350.1710.1820.0220.0360.1220.0480.4510.2720.4490.0511.0000.333
TIPO_DEFICIENCIA_ID0.000-0.0590.0370.2730.0170.018-0.0740.014-0.233-0.120-0.2320.0000.3331.000

Missing values

2025-05-21T16:38:23.295985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-21T16:38:23.599319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-21T16:38:23.934447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ATIVOPESSOA_FISICA_IDProntuário SIAP ?NUMERO_PRONTUARIONOME_COMPLETODATA_NASCIMENTOIDADE_ESTIMADANACIONALIDADE_IDNOME_MAENOME_PAINUMERO_CPFNUMERO_RGNUMERO_TEL_CELTIPO_DEFICIENCIA_IDESCOLARIDADE_IDESTADO_CIVIL_IDESTADO_ECONOMICO_IDPROFISSAO_IDSEXO_IDORIENTACAO_SEXUAL_IDIDENTIDADE_GENERO_IDSIGLA
0SELECT \n CASE \n WHEN det.ativo = 1 THEN 'ATIVO'\n ELSE TO_CHAR(det.ativo)\n END AS ATIVO,\n pf.PESSOA_FISICA_ID,\n det.DETENTO_ID as "Prontuário SIAP ?",\n det.NUMERO_PRONTUARIO,\n pf.NOME_COMPLETO,\n pf.DATA_NASCIMENTO,\n pf.IDADE_ESTIMADA,\n pf.NACIONALIDADE_ID,\n pf.NOME_MAE,\n pf.NOME_PAI,\n pf.NUMERO_CPF,\n pf.NUMERO_RG,\n pf.NUMERO_TEL_CEL,\n pf.TIPO_DEFICIENCIA_ID,\n --det.ENDERECO_ID,\n pf.ESCOLARIDADE_ID,\n pf.ESTADO_CIVIL_ID,\n pf.ESTADO_ECONOMICO_ID,\n pf.PROFISSAO_ID,\n pf.SEXO_ID,\n pf.ORIENTACAO_SEXUAL_ID,\n pf.IDENTIDADE_GENERO_ID,\n un.sigla\n\nFROM siap.detento det\nJOIN siap.pessoa_fisica pf ON pf.pessoa_fisica_id = det.pessoa_fisica_id\nJOIN siap.unidade un ON un.unidade_id = det.unidade_id\nWHERE un.sigla IN ('PATRONATO', 'URA')NaNNaNNaNNaNNaTNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1ATIVO168.0145.04005332MARIA LÚCIA IRACI NASCIMENTO DE LIMA1987-04-01 00:00:000.01.0SEVERINA IRACI DO NASCIMENTOJOÃO LÚCIO SOBRINHONaNNaNNaN6.04.01.02.06.02.0NaNNaNURA
2ATIVO171.0148.04005369MARIA DA CONCEIÇÃO DOS SANTOS1987-10-25 01:00:000.01.0MARIA FILOMENA DOS SANTOS0NaNNaNNaN6.02.05.0NaN6.02.0NaNNaNURA
3ATIVO173.0150.04005380GILVÂNIA MELQUÍADES DA SILVA1986-06-05 00:00:000.01.0GENILDA ALVES DA SILVAJOSÉ MELQUÍADES DA SILVANaNNaNNaN6.06.04.02.06.02.0NaNNaNPATRONATO
4ATIVO179.0156.04005439ERICA IRIS DE SANTANA ALBUQUERQUE1985-01-27 00:00:000.01.0MARTA MARIA DE SANTANAROBERTO KENNEDY DE ALBUQUERQUENaNNaN8.198520e+106.03.04.05.06.02.0NaNNaNPATRONATO
5ATIVO180.0157.04005446MAIZA SEVERINA DA SILVA1980-07-03 00:00:000.01.0SEVERINA MARIA DA SILVA0NaNNaN8.198375e+106.03.04.03.06.02.0NaNNaNPATRONATO
6ATIVO181.0158.04005447VALDILENE MARTINS DA SILVA1984-11-11 00:00:000.01.0MARIA GORETE MARTINS DA SILVA0NaNNaNNaN6.05.04.0NaN6.02.0NaNNaNURA
7ATIVO188.0165.04005491EDRIANA WILMA DA SILVA1977-06-20 00:00:000.01.0MARLEIDE DA SILVA0NaNNaN8.198982e+106.06.04.04.06.02.0NaNNaNURA
8ATIVO190.0167.04005501MARILENE CRISTINA DOS PRAZERES MENDES1977-01-14 00:00:000.01.0MARIA DOS PRAZERES MENDESMARCOS ANTÔNIO MENDESNaNNaNNaN6.06.04.04.06.02.0NaNNaNURA
9ATIVO199.0176.04005518SUZANA INÊS DOS SANTOS1979-10-08 00:00:000.01.0JOSEFA INÊZ DOS SANTOSSEBASTIÃO BELO DOS SANTOSNaNNaNNaN6.07.04.02.06.02.0NaNNaNURA
ATIVOPESSOA_FISICA_IDProntuário SIAP ?NUMERO_PRONTUARIONOME_COMPLETODATA_NASCIMENTOIDADE_ESTIMADANACIONALIDADE_IDNOME_MAENOME_PAINUMERO_CPFNUMERO_RGNUMERO_TEL_CELTIPO_DEFICIENCIA_IDESCOLARIDADE_IDESTADO_CIVIL_IDESTADO_ECONOMICO_IDPROFISSAO_IDSEXO_IDORIENTACAO_SEXUAL_IDIDENTIDADE_GENERO_IDSIGLA
46396ATIVO1033966.0580676.020250823ELIOMAR JOSE CABRERA BARRETO1992-07-17NaN1.0ELIOMAR VICITACION CABRERA BASTARDOPAI NÃO DECLARADO70912751282NaN8.286369e+095.07.05.02.0581.01.026.026.0PATRONATO
46397ATIVO1033977.0580696.020250841GUSTAVO ANTONIO DE LIRA1998-06-30NaN1.0MARIA DE LOURDES DE AMANCIO DE LIRAGILSON ANTONIO DE LIRANaNNaN8.198754e+105.06.04.02.0830.01.026.026.0PATRONATO
46398ATIVO1034583.0580936.020250899ANDREIA CARLA MARTINS CAVALCANTI1981-09-13NaN1.0GERUSA MARIA DA SILVA MARTINSDesconhecido/Não DeclaradoNaNNaNNaN5.08.01.02.0681.02.026.026.0PATRONATO
46399ATIVO1033982.0580698.020250853ALEX MANOEL DA SILVA1980-07-27NaN1.0OSANA IZABEL DA SILVAMANOEL ANTONIO DA SILVA04004806461NaN8.199274e+105.03.02.01.0738.01.026.026.0PATRONATO
46400ATIVO1034668.0580995.020250893MARIA IRIS NATALY COSTA E SILVA1990-03-08NaN1.0SUELY COSTA E SILVAPAI NÃO DECLARADO09965066400NaN8.198633e+105.07.04.01.0346.02.026.026.0PATRONATO
46401ATIVO1033975.0580695.020250829CRISTIANO GERONIMO DOS SANTOS1958-02-07NaN1.0OVIDIA GERONIMO DOS SANTOSNÃO DECLARADO19231040472NaN8.199645e+105.03.02.02.0651.01.026.026.0PATRONATO
46402ATIVO1033981.0580697.020250852NARTACHE DRIELLY ALVES CORREIA1991-04-26NaN1.0ANA MARIA CORREIAJOSE ALVES DOS SANTOS08652431400NaN8.198643e+105.07.04.09.0534.02.026.026.0PATRONATO
46403ATIVO1034103.0580718.020250870JOSUEL DOS SANTOS1976-08-09NaN1.0MARIA DIVANE DOS SANTOSAMARO FRANCISCO DOS SANTOS70221792473NaN8.199773e+105.015.05.01.0636.01.026.026.0PATRONATO
46404ATIVO1034765.0581036.020250905RAFAEL JORDAO ACCIOLY1996-01-02NaN1.0SILENE JORDAO DE OLIVEIRAEVAN JOSE DE BARROS ACCIOLY FILHONaNNaNNaN5.06.04.02.0659.01.026.026.0PATRONATO
46405ATIVO1034783.0581037.020250908RICARDO FREDERICO DA ROCHA E SILVA1982-11-13NaN1.0CREUZA MARIA DE OLIVEIRAFELIX NOLE DA ROCHA E SILVA FILHO04595924402NaN8.199623e+105.06.04.03.0851.01.026.026.0PATRONATO